Fingerprinting System Research Articles

Automating Student Attendance Through Face Recognition and Machine Learning Techniques

In recent years, facial detection and recognition technologies have become increasingly important in various fields, including smartphones, defence, and secure information centres. Acknowledging their potential, we intend to create a device that uses facial recognition as an effective and innovative substitute for conventional attendance methods, such as paper and fingerprint systems. The primary goal of this project is to establish a unified attendance system that employs facial recognition technology to confirm student identities and simplify the attendance process. This method provides a smooth, secure, and precise solution for contemporary academic settings. The system employs a machine learning algorithm developed for use in Python, allowing it to take pictures of students using the camera on a computer or laptop. Moreover, external cameras can be utilized as long as they are linked to the system. The face recognition capability is implemented through the Haarcascade algorithm, which is incorporated into the software.

Wi-Fi CSI fingerprinting-based indoor positioning using deep learning and vector embedding for temporal stability

Fingerprinting systems based on channel state information (CSI) often rely on updated databases to achieve indoor positioning with high accuracy and resolution of centimeter-level. However, regularly maintaining a large fingerprint database is labor-intensive and computationally expensive. In this paper, we explore the use of deep learning for recognizing long-term temporal CSI data, wherein the site survey was completed weeks before the online testing phase. Compared to other positioning algorithms such as time-reversal resonating strength (TRRS), support vector machines (SVM), and Gaussian classifiers, our deep neural network (DNN) model shows a performance improvement of up to 10% for multi-position classification with centimeter-level resolution. We also exploit vector embeddings, such as i-vectors and d-vectors, which are traditionally employed in speech processing. With d-vectors as the compact representation of CSI, storage and processing requirements can be reduced without affecting performance, facilitating deployments on resource-constrained devices in IoT networks. By injecting i-vectors into a hidden layer, the DNN model originally for multi-position localization can be transformed to location-specific DNN to detect whether the device is static or has moved, resulting in a performance boost from 75.47% to 80.62%. This model adaptation requires a smaller number of recently collected fingerprints as opposed to a full database.

Microbial Sampling and Analysis Taken from Fingerprint System Devices Located at Al-Yarmouk Teaching Hospital in Baghdad - Iraq

Biometric devices (BDs) are nowadays common in use for a variety of purposes. This system involves physical contact between the skin and surface of the device, which is likely to be contaminated by microorganisms of multiple users. However, it can be a source of multiple microbiological contaminations. This study was done at AL-Yarmouk Teaching Hospital-Baghdad- Iraq, during the period started from 15th November 2023 till 15th March 2024. It was designed to find out the most common bacteria and fungi that might contaminate the fingerprint devices and performing antimicrobial susceptibility tests. Forty-six sterile swab samples taken from Twenty-three fingerprint system devices distributed in twelve different hospital departments. From each device we took two swabs, one before sterilization other after, and tested sensitivity patterns against 16 antimicrobials. Four Gram positive bacteria scored on fingerprint devices, in Medicine, Surgery, Orthopedics and Obstetrics & Gynecology departments represented by Coagulase-negative Staphylococci (CoNS). Response to sensitivity test for antibiotics, especially Moxifloxacin, Linezolid, Teicoplanin, Vancomycin, Tigecycline and Rifampicin, 100%, as well other antibiotics but in varying degrees, and not any type of fungi were isolated. In conclusion, Gram positive bacteria population was the most common organisms, and highly response in varying degrees to the most Sixteen antibiotics used in sensitivity test, however, should proper maintenance and educational practices must be applied to minimize contamination.

Penerapan Fingerprint dalam Meningkatkan Karakter Disiplin Siswa di SMA Negeri 1 Luragung

The purpose of the study was to analyze how the effectiveness of the implementation of fingerprint attendance at SMAN 1 Luragung on improving the character of student discipline at SMAN 1 Luragung. The method used in this study is a qualitative method with a qualitative design of a descriptive type. The research participants were 10 students in grade XI. The data collection technique uses interviews, observations, and documentation while the instrument used is an interview sheet. Data analysis techniques by means of data reduction, data presentation and verification or conclusion drawing. The results of the study show that the use of fingerprint attendance at SMA Negeri 1 Luragung is generally quite effective. This technique helps create a more organized and comfortable learning environment by improving student discipline and attendance management in general. The level of student attendance discipline at SMA Negeri 1 Luragung has increased significantly. Students become more responsible, punctual, and understand that discipline is the key to success, both in school and in everyday life. The implementation of the fingerprint system at SMA Negeri 1 Luragung has a significant effect on improving the character of student discipline. This system makes students more aware of the importance of responsibility and punctuality, thereby reducing the rate of tardiness and absenteeism.

Genetic Diversity Analysis and Fingerprint Construction for 87 Passionfruit (Passiflora spp.) Germplasm Accessions on the Basis of SSR Fluorescence Markers

A comprehensive genetic diversity analysis of 87 Passiflora germplasm accessions domesticated and cultivated for several years in the karst region of Guizhou, China, was conducted utilizing simple sequence repeat (SSR) fluorescent markers. These Passiflora species, renowned for their culinary and medicinal value, could bring significant economic and ecological benefits to the region. This study aimed to assess the genetic resources of these species and facilitate the selection of superior cultivars adapted to the karst environment. Our analysis revealed an abundance of SSR loci within the Passiflora transcriptome, with single-base repeats being the most prevalent type. Through rigorous primer screening and amplification, we successfully identified 27 SSR primer pairs exhibiting robust polymorphisms. Further interrogation at eight microsatellite loci revealed 68 alleles, underscoring the high level of genetic diversity present in the cultivated accessions. The average expected heterozygosity was 0.202, with the ssr18 locus exhibiting the highest value of 0.768, indicating significant genetic variation. The mean polymorphic information content (PIC) of 0.657 indicates the informativeness of these SSR markers. Comparative analyses of the cultivated and potential wild progenitors revealed distinct genetic variations among the different Passiflora types. Genetic structure and clustering analyses of the 87 accessions revealed seven distinct groups, suggesting gene flow and similarities among the resources. Notably, a DNA fingerprinting system was established using eight SSR primer pairs, effectively distinguishing the selected cultivars that had adapted to the karst mountainous region. This study not only deepens our understanding of Passiflora genetic resources in the karst environment but also provides a valuable reference for conservation, genetic improvement, and cultivar selection. The rich genetic diversity of the Passiflora germplasm underscores their potential for sustainable utilization in breeding programs aimed at enhancing the economic and ecological viability of these valuable plant species.

Effective Payroll Procedures Enhanced by Electronic Attendance Recording in Indonesia (Prosedur Penggajian yang Efektif Ditingkatkan dengan Pencatatan Kehadiran Elektronik di Indonesia)

This research investigates the effectiveness of accounting information systems in the payroll cycle of a company. Utilizing purposive sampling for interviews, the study evaluates the organizational structure's separation of responsibilities and payroll procedures, including staffing, attendance recording, payroll creation, and financial functions. Findings reveal that these practices are effectively implemented, aligning with theoretical expectations. However, an evaluation suggests that incorporating electronic attendance recording, such as fingerprint systems, could further enhance accuracy and reduce errors in attendance and overtime tracking.

Survey on the convergence of learning techniques for precise pattern identification in fingerprint analysis

Fingerprint analysis has been an essential component of forensic science and biometric authentication systems. The precise recognition of fingerprint patterns is critical for solving crimes, confirming identities and assuring the security of numerous applications. In recent years, the convergence of learning approaches, particularly machine learning and deep learning, has played an essential part in improving the accuracy and efficiency of fingerprint pattern recognition. In this paper, we discuss recent advancements in physiological-based biometric multimodalities, with a particular focus on the field of precise pattern identification in fingerprint analysis. Additionally, we assume the task of summarizing and examining a range of physiological-based biometric modalities, encompassing both traditional and deep learning approaches. A detailed review of several biometric measurements across many modalities is presented, encompassing various phases, including preprocessing, feature extraction and classification, which are thoroughly discussed. We provide a comprehensive analysis of the challenges and future developments associated with conventional and deep learning methodologies. The objective is to enable investigators to recognize these problems. A review is conducted to evaluate the standard and deep learning approaches employed in different physiological-based biometric systems. The comparative analysis of this review suggests that more advancement is necessary for the development of a reliable physiological-based approach to enhance and optimize the functionality of the fingerprint system.

Internalisasi Nilai-Nilai Pendidikan Islam Dalam Transaksi Non Tunai (Virtual Account Dan Fingerprint) TMI Putri Al-Amien Prenduan

Al-Amien Prenduan Islamic boarding school issued a new breakthrough in an effort to prevent corruption and theft. Everything that is done by the Al-Amien Prenduan Islamic boarding school aims to instill the value of Islamic education to female students. Since 2019, TMI has required all its students to use digital money in buying and selling transactions. Where parents send money not in the form of cash but through (virtual accounts) so that students use fingerprints in buying and selling transactions.In this study, researchers used a descriptive qualitative approach. To find out about the implementation of non-cash transactions in order to increase the internalization of Islamic educational values at the TMI Putri Al-Amien Prenduan Islamic boarding school. Data collection techniques include observation, interviews and documentation.The results of this study are honest attitude, patient attitude, not wasteful, thrifty and mandate is a form of instillation of Islamic educational values in non-cash transactions.as for internal and external support is a supporting factor while the lack of knowledge of student guardians related to the fingerprint system makes student guardians do not limit remittances every month.

Innovative Solutions for Worn Fingerprints: A Comparative Analysis of Traditional Fingerprint Impression and 3D Printing.

Fingerprint recognition systems have achieved widespread integration into various technological devices, including cell phones, computers, door locks, and time attendance machines. Nevertheless, individuals with worn fingerprints encounter challenges when attempting to unlock original fingerprint systems, which results in disruptions to their daily activities. This study explores two distinct methods for fingerprint backup: traditional fingerprint impression and 3D printing technologies. Unlocking tests were conducted on commonly available optical fingerprint lock-equipped cell phones to assess the efficacy of these methods, particularly in unlocking with worn fingerprints. The research findings indicated that the traditional fingerprint impression method exhibited high fidelity in reproducing fingerprint patterns, achieving an impressive unlocking success rate of 97.8% for imprinting unworn fingerprints. However, when dealing with worn fingerprints, the traditional fingerprint impression technique showed a reduced unlocking success rate, progressively decreasing with increasing degrees of finger wear. In contrast, 3D-printed backup fingerprints, with image processing and optimization of ridge height, mitigated the impact of fingerprint wear on the unlocking capability, resulting in an unlocking success rate of 84.4% or higher. Thus, the utilization of 3D printing technology proves advantageous for individuals with severely worn or incomplete fingerprints, providing a viable solution for unforeseen circumstances.

Smart Power Saving and Safety Management system Based on Smart Sensors Technologies

Comfort is becoming a major priority in the 21st century. So the revolutions of computing and smart environment came into existence. Some technologies like Ubiquitous/pervasive and ambient intelligence satisfy the maximum need of smart world but these technologies are not tightly coupled with the internet, so the people need another technology extension. Automation based on energy saving and safety is an ideal buzzing technology to influence new world technologies. In this project, an overview of smart automation systems is discussed. This project presents a design and prototype of the Automation system, With Motion sensor, LDR sensor , Fingerprint and smart theft detection system is introduced. In addition to that, LCD display would be used which will allow the user to monitor their devices smartly and gets information of security. Top of Form Keywords— Automation, Arduino Controller, LCD display, sensors , Load , Buzzer etc.

Personalized user authentication system using wireless EEG headset and machine learning

In the realm of authentication, biometric verification has gained widespread adoption, especially within high-security user authentication systems. Although convenient, existing biometric systems are susceptible to a number of security vulnerabilities, including spoofing tools such as gummy fingers for fingerprint systems and voice coders for voice recognition systems. In this regard, brainwave-based authentication has emerged as a novel form of biometric scheme that has the potential to overcome the security limitations of existing systems while facilitating additional capabilities, such as continuous user authentication. In this study, we focus on a data-driven approach to Electroencephalography (EEG)-based authentication, guided by the power of machine learning algorithms. Our methodology addresses the fundamental challenge of distinguishing real users from intruders by training classification algorithms to the unique EEG signatures of every individual. The system is characterized by its convenience, ensuring real-time applicability without compromising its efficiency. By employing a commercially available single-channel EEG sensor and extracting a set of 8 power spectral features (delta [0–4 Hz], theta [4–8 Hz], low alpha [8–10 Hz], high alpha [10–12 Hz], low beta [12–20 Hz], high beta [20–30 Hz], low gamma [30–60 Hz], high gamma [60–100 Hz]), a commendable mean accuracy of 85.4% was achieved.

IoT-based Facelook and Fingerprint Safe Security System

This research aims to develop an advanced safe security system by combining Facelook and Fingerprint technologies based on the Internet of Things (IoT). These technologies are expected to provide a higher level of security and facilitate access for safe owners. Subsequently, testing is carried out on the safe's opening mechanism after successful authentication via both the fingerprint sensor and the face recognition system. These trials encompass the evaluation of success rates, the speed of the opening mechanism, and the overall response time. Data from the testing phase is collected and analyzed to comprehensively assess the system's performance. The average notification delivery delay for the face recognition system was measured at 2.67 seconds with a standard deviation of 0.37. The notification delivery data revealed an average delay of 2.04 seconds with a standard deviation of 0.38 These findings collectively affirm the effectiveness of the integrated face recognition and fingerprint system in the proposed safe security setup

SMA Kosgoro Bogor's Student Attendance Accountability Through Finger Print That Led to Appreciation

SMA Kosgoro Bogor uses fingerprint technology as a tool to improve accountability of student attendance and monitoring. The implementation of fingerprint technology has enabled the school to have detailed and automated information about student attendance. This article aims to explain how the use of the fingerprint system at SMA Kosgoro Bogor can improve student attendance accountability. The method used in this research is a descriptive qualitative method that produces descriptive data in the form of written or oral observations of people and observable behaviour. The data source of this research uses primary data sources and secondary data. The research subjects were all students of SMAS Kosgoro Bogor. One form of appreciation given is the provision of free tuition scholarships at the beginning of each month that meets predetermined criteria, such as consistent daily attendance, recorded in the school attendance system, designated as a nominee by the homeroom teacher, active in extracurricular activities, speaking well, and behaving politely.

Advancements in ATM Security: A Biometric Approach for Fingerprint and Face Recognition Access Control System

Abstract: The project, introduces an innovative approach to enhance the security and accessibility of Automated Teller Machines (ATMs). In an era where financial transactions are increasingly digital and essential, safeguarding ATM access is of paramount importance. This project leverages the power of biometric authentication, combining fingerprint and face recognition technologies to create a robust and reliable access control system. The proposed system allows ATM users to authenticate their identity with a seamless and secure process. Fingerprint recognition provides a high-precision, individualized identification method, while face recognition adds an additional layer of security, ensuring the users identity matches the stored biometric data. This dual-biometric approach significantly reduces the risk of unauthorized access and fraudulent activities at ATMs. Beyond enhancing security, the project contributes to user convenience by streamlining the authentication process, eliminating the need for traditional ATM cards or PINs. Users can access their accounts swiftly and securely, thereby improving the overall ATM experience. Furthermore, the project’s incorporation of state-of-the-art biometric technologies underscores its potential to set a standard for secure ATM access not only in the financial sector but also in various other domains where access control is critical.

Program Perbaikan Data Absensi Fingerprint di Perusahaan Alfa Premium New Star

Nowadays, every company has implemented sophisticated technology and keeps up with the times, such as manual attendance being switched to fingerprint, which no one can manipulate. The fingerprint attendance system is used to manage employee absences in various companies. The company Alfa Premium New Star currently uses 2 attendance systems, the manual system being the time clock and the fingerprint machine. The fingerprint system makes it easier for company admins to carry out overall employee attendance. However, the current implementation of the machine is not without its shortcomings. The program we developed uses an algorithm to handle problems by analyzing existing attendance data.

Line image sensor-based colony fingerprinting system for rapid pathogenic bacteria identification

The rapid identification of pathogenic bacteria is crucial across various industries, including food or beverage manufacturing. Bacterial microcolony image-based classification has emerged as a promising approach to expedite identification, automate inspections, and reduce costs. However, conventional imaging methods have significant practical limitations, namely low throughput caused by the limited imaging range and slow imaging speed. To address these challenges, we developed an imaging system based on a line image sensor for rapid and wide-field imaging compared to existing colony imaging methods. This system can image a standard Petri dish (92 mm in diameter) completely within 22 s, successfully acquiring bacterial microcolony images. This process yielded a set of discrimination parameters termed as colony fingerprints, which were employed for machine learning. We demonstrated the performance of our system by identifying Staphylococcus aureus in food products using a machine learning model trained on a colony fingerprint dataset of 15 species from 9 genera, including foodborne pathogens. While conventional mass spectrometry-based methods require 24 h of incubation, our colony fingerprinting approach achieved 96% accuracy in just 10 h of incubation. Line image sensor offer high imaging speeds and scalability, allowing for swift and straightforward microbiological testing, eliminating the need for specialized expertise and overcoming the limitations of conventional methods. This innovation marks a transformative shift in industrial applications.

Masked Face Recognition Using FaceNet Algorithm

In the past three years, the entire world has been exposed to a new virus, COVID-19. It has become very difficult to deal with the fingerprint system, as the exchange of things and contact with devices by more than one person is a good way to transmit the virus. Accordingly, many institutions and organizations have resorted to using face prints as an alternative to handprints to identify people and record the attendance of employees in general. Since the virus is a virus that infects the respiratory system, people are forced to wear face masks or the so-called masks to avoid transmission of the virus during coughing by the infected person. Since the wearing of masks greatly affects the facial features, it was necessary to come up with a technology that allows the identification of masked faces, and this is the subject of our study. Since the face loses many of its features while wearing masks, an algorithm was proposed to recognize the face and train the convolutional neural networks (CNN) through some of the main facial features, including 3D imaging, as it was concluded that the triple loss does not apply to our data sets, as 3D selection has less loss compared to 2D image, Due to its ability to select all feature samples from feature spaces with larger distances between layers and reduced distances between regions, a large cosine loss was utilized as the training loss function. In order to reach a model that deals more with areas not covered by the mask, the input unit was designed, as its function is to combine the Inception- Resent unit and the Convolutional Mass unit, and so, any portion of the face that isn't covered gains weight, thus increasing the importance of those areas in the recognition of masked faces, and experiments that were conducted on several sets of masked faces data showed that the algorithm works to significantly increase the accuracy of masking face recognition, and it can accurately recognize the face using masks.

GShock: A GNN-Based Fingerprinting System for Permissioned Blockchain Networks Over Encrypted Channels

gShock: A GNN-Based Fingerprinting System for Permissioned Blockchain Networks Over Encrypted Channels

Impact of Finger Type in Contactless Fingerprint Verification

Contactless fingerprint authentication has gained popularity as a field of research in biometrics in recent years. Unlike traditional fingerprint recognition systems that require direct contact of the person’s finger with the sensor, contactless fingerprint systems offer several advantages, among them ease of capture and cost-effectiveness. Despite the progress made in this field, poor contrast, background noise, and limited image information continue to pose difficulties for fingerprint recognition in contactless environments. Furthermore, the number of images in published fingerprint biometric datasets for each person is restricted, and there is insufficient data for efficient training. Nevertheless, Convolutional Neural Network (CNN) architectures have been widely used, necessitating large databases. To address these issues, this paper introduces a Siamese network designed for the purpose of identity Verification, using the contactless thumb fingerprint modality to enhance recognition results. The Siamese network is able to extract pertinent features from noisy images with low contrast and limited information, even if they have limited information and low contrast. Additionally, this work proposes the use of the contactless thumb fingerprint modality instead of the contactless index fingerprint modality, which is more commonly used in related works. Consequently, the Mobile FingerPrint (MFP) dataset is introduced and constructed for evaluation. Experimental results demonstrate the efficiency of the proposed method, achieving an accuracy of 98.68% for thumb fingerprint Verification.

Deep learning techniques for biometric security: A systematic review of presentation attack detection systems

Biometric technology, including finger vein, fingerprint, iris, and face recognition, is widely used to enhance security in various devices. In the past decade, significant progress has been made in improving biometric systems, thanks to advancements in deep convolutional neural networks (DCNN) and computer vision (CV), along with large-scale training datasets. However, these systems have become targets of various attacks, with presentation attacks (PAs) being prevalent and easily executed. PAs involve displaying videos, images, or full-face masks to trick biometric systems and gain unauthorized access. Many authors are currently focusing on detecting these presentation attacks (PAD) and have developed several methods, particularly those based on deep learning (DL), which have shown superior performance compared to other techniques. This survey article focuses on manuscripts related to deep learning presentation attack detection, spoof attack detection using deep learning, and anti-spoofing deep learning methods for biometric finger vein, fingerprint, iris, and face recognition. The studies were primarily sourced from four digital research libraries: ACM, Science Direct, Springer, and IEEE Xplore. The article presents a comprehensive review of DL-based PAD systems, examining recent literature on DL-based PAD methods in finger vein, fingerprint, iris, and face detection systems. Through extensive research of the literature, recent algorithms and their solutions for relevant PAD approaches are thoroughly analyzed. Additionally, the article provides a performance analysis and highlights the most promising research findings. The discussion section addresses current issues, opportunities for advancement, and potential solutions associated with deep learning-based PAD methods. This study is valuable to various community users seeking to understand the significance of this technology and its recent applicability in the development of biometric technology for deep learning.